Nowadays, inspection process planning (IPP) for large volume metrology (LVM) attracts increasing attention in manufacturing and assembly industries such as aerospace and automotive, where large and complex assemblies and fabrications with complex surfaces are employed. Inspection is conventionally considered as a quality control manner. But there is changing shift to processes that are more related to the early design stage aiming to increase product performance and reduce costs by automation and elimination of rework. This is especially evident in the standardisation and implementation of Geometric Dimensioning and Tolerancing (GD&T) of new products and systems at the design stage. This study proposes a GD&T based systematic framework for the IPP of LVM systems within a digital environment. Orientating to solve the “what to measure” and “how to measure” problems in IPP, the prototype system has seven functional core modules including: tolerance feature analysis, instrument selection, inspection point selection, accessibility and visibility analysis, instrument setup and configuration, clustering analysis and measurement sequencing. An optimized inspection plan is output for the designer to evaluate the product design as well as for guiding the metrologist and process planner to conduct the inspection process. Heuristic rules, evolutionary algorithms and modern computational graphic techniques have been adopted to facilitate the supported functions. Coupled with state of art metrology systems, metrology and CAD software, the framework is able to work effectively and efficiently by means of incorporating international standards and industrial best practice. It is the first attempt to successfully minimise manual activities in the planning process for LVM systems, which results in improved efficiency, enhanced decision making and a better inspection plan overall. Two case studies have been conducted to validate the functionalities of the prototype system.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:681030 |
| Date | January 2013 |
| Creators | Cai, Bin |
| Contributors | Maropoulos, Paul |
| Publisher | University of Bath |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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